Abstract: Zero-Shot Temporal Action Localization (ZS-TAL) seeks to identify and locate actions in untrimmed videos unseen during training. Existing ZS-TAL methods involve fine-tuning a model on a large amount of annotated training data. While effective, training-based ZS-TAL approaches assume the availability of labeled data for supervised learning, which can be impractical in some applications. Furthermore, the training process naturally induces a domain bias into the learned model, which may adversely affect the model's generalization ability to arbitrary videos. These considerations prompt us to approach the ZS-TAL problem from a radically novel perspective, relaxing the requirement for training data. To this aim, we introduce a novel method that performs $\textbf{T}$est-$\textbf{T}$ime adaptation for $\textbf{T}$emporal $\textbf{A}$ction $\textbf{L}$ocalization ($\textbf{T3AL}$). In a nutshell, $T3AL$ adapts a pre-trained Vision and Language Model (VLM) at inference time on a sample basis. $T3AL$ operates in three steps. First, a video-level pseudo-label of the action category is computed by aggregating information from the entire video. Then, action localization is performed adopting a novel procedure inspired by self-supervised learning. Finally, frame-level textual descriptions extracted with a state-of-the-art captioning model are employed for refining the action region proposals. We validate the effectiveness of $T3AL$ by conducting experiments on the THUMOS14 and the ActivityNet-v1.3 datasets. Our results demonstrate that $T3AL$ significantly outperforms zero-shot baselines based on state-of-the-art VLMs, confirming the benefit of a test-time adaptation approach.